Core Viewpoint - The World Artificial Intelligence Conference (WAIC) highlighted the strategic importance of AI for Science (AI4S), marking it as one of the ten core directions with dedicated forums and discussions, indicating its transformative role in reshaping scientific foundations [3][4]. Group 1: AI for Science (AI4S) Development - AI for Science has gained significant attention, especially after AlphaFold's success in solving long-standing biological challenges, demonstrating its real-world impact [3]. - The "Starry River Enlightenment" forum, co-hosted by Fudan University and the Shanghai Institute of Intelligent Science, served as a platform for discussing the trends and innovations in AI for Science [4][5]. - The forum gathered global experts, including Turing and Nobel Prize winners, to explore collaborative innovation and industrial practices in the AI4S 2.0 era [5]. Group 2: Open Collaboration and Ecosystem Building - Fudan University emphasized the need for an open scientific ecosystem, moving beyond the "tool mindset" to a collaborative "ecological mindset" involving human scientists and AI [7]. - The "Open Science Global Academic Cooperation Initiative" was launched to address the challenges of data disparity and promote a collaborative global scientific ecosystem [31][34]. - The initiative proposes four core actions: building open infrastructure, initiating large-scale scientific projects, fostering talent development, and creating a new era of human science [34]. Group 3: Educational and Research Paradigms - The dialogue among university leaders focused on how universities will be reshaped in the AI4S 2.0 era, emphasizing the transition from a "tool mindset" to an "ecological mindset" [39][40]. - The importance of foundational research in AI was highlighted, with calls for strengthening education in mathematics and physics to cultivate top AI talent [40]. - The need for a transformation in university structures and evaluation systems was recognized to adapt to the evolving landscape of scientific intelligence [40]. Group 4: Industry and Academic Collaboration - The forum discussions revealed a consensus on the necessity for collaboration among industry, academia, and new research institutions to foster a thriving ecosystem for AI4S [44]. - Industry representatives pointed out the mismatch between AI model generation and experimental validation, advocating for automated laboratories to bridge this gap [45]. - Academic perspectives focused on enhancing model learning capabilities and addressing ethical concerns related to AI applications in sensitive fields like life sciences [47]. Group 5: Practical Applications and Ethical Governance - The "Starry River Enlightenment" platform was introduced as a comprehensive system to empower scientists by providing open data, shared models, and automated experimental capabilities [53]. - Specific applications showcased the potential of AI in various fields, including life sciences and humanities, demonstrating its broad impact [55][56]. - Ethical governance was emphasized as crucial for the sustainable development of the ecosystem, with initiatives to enhance the efficiency and professionalism of ethical reviews in research [66][68].

Core Insights - The WAIC2025 Star River Intelligent Open Cooperation Forum was held, focusing on building an open and collaborative scientific intelligence ecosystem [1] - Multiple innovative achievements were announced, including the Early Chinese Civilization Multimodal Large Model and the Global Academic Cooperation Initiative [1][3] Group 1: Early Chinese Civilization Multimodal Large Model - The Early Chinese Civilization Multimodal Large Model was officially released, developed by Fudan University, Shanghai Intelligent Research Institute, and Shanghai Chuangzhi Academy [3] - The model encompasses 100TB of specialized corpus, SFT data, and evaluation sets, pioneering cross-modal intelligent alignment of civilization spatiotemporal data [3] - It supports the Chinese Civilization AI Agent platform, enabling multi-step reasoning and complex task planning, benefiting education, research, and cultural industries [3] Group 2: Global Academic Cooperation Initiative - A global initiative was launched by top international scientists, including Nobel Prize winners, aiming to break the "data divide" and ensure AI benefits reach every corner of the globe [3] - The initiative outlines four core objectives: building open-source scientific infrastructure, initiating multinational interdisciplinary scientific programs, cultivating international scientific talent, and establishing a fair value-sharing mechanism [3][4] Group 3: Star River Intelligent Open Platform - The Star River Intelligent Open Platform was launched, designed to accelerate scientific discovery and provide comprehensive infrastructure for scientists and AI engineers [6] - It aims to enhance cross-disciplinary collaboration and address key scientific challenges, significantly speeding up scientific discoveries [6] - The platform includes the "Guanxin" large model, which formalizes complex clinical diagnosis processes into a multi-agent collaborative system for cardiovascular specialties [6] Group 4: Ethical Review AI Agent "Yijian" - The ethical review AI agent "Yijian" was introduced, capable of automatic rule review and risk labeling, enhancing review efficiency and compliance [7] - It has been trialed at Fudan University and its affiliated Zhongshan Hospital, ensuring data security and supporting the generation of review reports [7]

Core Viewpoint - The competition among top-tier hospitals in China has intensified in the AI sector, with a significant focus on developing medical AI models to enhance healthcare services and operational efficiency [1][3]. Group 1: AI Model Development - As of mid-2023, approximately 300 medical AI models have been developed in China, with nearly half released in the first half of the year [3]. - Major hospitals like Shanghai Zhongshan, Ruijin, Renji, and Xinhua have launched AI models targeting various medical fields, including cardiology and pediatrics [1][3]. - The RuiPath pathology model, developed by Ruijin Hospital in collaboration with Huawei, has been recognized internationally for its capabilities in AI-assisted pathology diagnosis [4]. Group 2: AI Applications in Healthcare - AI applications in hospitals are expanding, with digital guides and AI models being utilized for patient consultations and decision-making support [3][4]. - The "CardioMind" model from Fudan University Zhongshan Hospital aims to enhance cardiology diagnostics and treatment, leveraging extensive patient data [5]. - AI models are expected to handle up to 80% of routine tasks, allowing doctors to focus on complex cases and patient interactions [7]. Group 3: Challenges and Ethical Considerations - The rapid advancement of AI technology poses challenges, including the need for robust data governance and ethical standards in medical AI applications [8][9]. - Concerns regarding the accuracy and reliability of general AI models in specialized medical fields have been raised, highlighting the importance of using validated technologies [8]. - Ensuring patient data security and privacy is critical, with measures such as data anonymization and psychological support being implemented in AI model development [8].

Core Insights - The healthcare industry is rapidly adopting AI models to create an "autonomous driving" system for medical practices, with top-tier hospitals competing in AI capabilities [1][6] - In the first half of this year, approximately 300 medical AI models have been developed in China, with nearly half released in this timeframe, indicating a significant trend towards AI integration in healthcare [3] - AI applications in hospitals are expanding beyond simple tasks, with digital guides and AI models being utilized for various medical specialties, enhancing efficiency and patient care [3][4] Group 1: AI Model Development - Major hospitals like Zhongshan, Ruijin, Renji, and Xinhua have launched AI models for various diseases, including cardiology and pediatrics, showcasing the competitive landscape [1][3] - The RuiPath pathology model, developed by Ruijin Hospital in collaboration with Huawei, has been recognized internationally for its capabilities in AI-assisted pathology diagnosis [3][4] - The "CardioMind" model from Zhongshan Hospital represents a significant advancement in cardiology, aiming to provide expert-level diagnostic support to physicians [4][5] Group 2: AI Applications and Impact - AI models are being integrated into clinical workflows, with applications in clinical decision support, pre-consultation, medical record generation, and imaging diagnostics, accounting for 53% of usage scenarios [3] - The establishment of Tsinghua AI Agent Hospital illustrates the potential for fully automated healthcare environments, where AI can handle diagnostic tasks with high accuracy [6] - The use of AI in hospitals is expected to allow physicians to focus more on complex cases, as AI can manage up to 80% of routine tasks [6] Group 3: Challenges and Considerations - The rapid advancement of AI technology poses challenges in data management and ethical considerations, particularly regarding patient privacy and data security [7][8] - Hospitals face difficulties in accessing and utilizing high-quality data for training AI models, as much of this data is contained within closed systems [7][8] - The need for regulatory frameworks to keep pace with technological advancements in AI healthcare applications is becoming increasingly critical [7]